Background on Genetic Algorithms

This chapter introduces evolutionary computation/genetic algorithms starting at a high level. It uses the schema sampling theorem to provide an intuitive understanding for how evolution, operating on a population of chromosomes (symbol strings), will produce offspring that contain variants of the symbol patterns in the more fit parents each generation, and shows how the recombination operators will be biased for and against some patterns. The No Free Lunch (NFL) theorem of Wolpert and Macready for optimization search algorithms has shown that over the space of all possible problems, there can be no universally superior algorithm. Hence, it is incumbent on any algorithm to attempt to identify the domain of problems for which it is effective and try to identify its strengths and limitations. In the next section, we introduce Eshelman’s CHC genetic algorithm and recombination operators that have been developed for bit string and integer chromosomes. After showing its strengths particularly in dealing with some of the challenges for traditional genetic algorithms, its limitations are also shown. The final section takes up the application of CHC to subset selection problems, a domain of considerable utility for many machine learning applications. We present a series of empirical tests that lead us to the index chromosome representation and the match and mix set-subset size (MMX_SSS) recombination operator that seem well suited for this domain. Variants are shown for when the size of the desired subset is known and when it is not known. We apply this algorithm in later chapters to the feature subset selection problem that is key to our application of developing a speech-based diagnostic test for dementia.